2026-01-09 21:01:05
Welcome to the Free edition of How They Make Money.
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It’s the single most persistent question in the market today. CapEx is ramping, valuations are soaring, and the hype is deafening. Yet the earnings are real, margins are expanding, and demand is unprecedented.
That’s the tension Howard Marks addresses in his latest memo.
Marks is the co-founder of Oaktree Capital and one of the most respected investors of our time. His memos are thoughtful, long-cycle reflections on risk, psychology, and capital allocation.
Warren Buffett once put it plainly:
“When I see memos from Howard Marks in my mail, they’re the first thing I open and read. I always learn something.”
In his new memo, Marks cuts through the noise surrounding the AI boom. Rather than trying to forecast prices or call a market top, he examines behavior.
There are really two conversations happening at once:
• The scale and pace of the AI infrastructure buildout.
• How that buildout is being priced in public and private markets.
Marks’ conclusion is deliberately measured, but unmistakably cautious. If AI enthusiasm doesn’t produce a bubble, he writes, it will be a first.
In this article, we’ll pull out the memo’s most important insights and pair them with visuals to help frame where we are in the cycle.
Not to call a top. Not to predict what happens next. But to understand how bubbles work and what to do about them.
Today at a glance:
How Bubbles Form
Two Types of Bubbles
The Uncertainty Stack
The Debt Problem
How Investors Should Think
Bubbles don’t begin with bad ideas.
They begin with good ideas taken too far.
A new technology appears and feels genuinely different. It promises to change how the world works. Early adopters are rewarded, often dramatically. Those early successes validate the story, and that’s when excitement turns into FOMO (fear of missing out). This is true for both the industry (AI chips) and the market (AI stocks).
People stop asking “Is this real?” and start asking “How do I get exposure?”
Skepticism feels costly. Caution feels like falling behind.
CEOs, from Mark Zuckerberg and Sundar Pichai, have made clear that underinvesting in AI is a far greater risk than overinvesting. The danger arises when individual investors adopt a similar mindset, even when they face no existential threat.
I recreated this classic market psychology chart (the original from Wall Street Cheat Sheet is hard to read, and the website is no longer maintained). It captures the raw emotions that repeat across every market cycle. I’ll leave it to you to decide where we are today.
At the core, bubbles aren’t caused by technology itself.
They’re caused by excessive optimism applied to something new.
Newness matters because it removes constraints. With no history to anchor expectations, the future feels limitless. In turn, valuations stretch beyond what can be justified by predictable earnings power, not because investors are foolish, but because imagination fills the gaps where data doesn’t yet exist.
That’s why bubbles follow such a consistent pattern.
Human psychology doesn’t change.
An early success breeds confidence.
Confidence turns into extrapolation.
Extrapolation invites speculation.
Speculation lowers standards.
Eventually, prices stop reflecting likely outcomes and start reflecting potential.
One of Howard Marks’ most important observations is that debating whether something is a bubble can distract from better judgment. You don’t need a label to behave intelligently. You just need to recognize when the pendulum has swung too far.
That’s usually when we see:
IPOs and private funding rounds surge, often at extreme valuations.
Narratives overpower fundamentals, with stories running ahead of cash flows.
Returns concentrate in a few stocks, pulling in passive and momentum capital.
FOMO-driven speculation replaces risk assessment.
Financial engineering fills the gaps through leverage or circular deals.
All of those ingredients are undeniably present in today’s market.
As Sir John Templeton famously warned, the four most dangerous words in investing are: “This time it’s different.”
AI may indeed be transformative, and the massive CapEx ramp justified.
But when success is priced as inevitable, future returns tend to disappoint.
Not all bubbles are the same.
One of the most useful distinctions in Marks’ memo comes from a simple idea: some bubbles form around real technological inflections, while others are built almost entirely on financial excess.
Understanding which one you’re dealing with matters far more than debating whether something is “a bubble” at all.
These are the destructive kind.
They’re fueled by financial engineering, leverage, or the promise of returns without risk. Nothing fundamental has changed in the real economy. When enthusiasm fades, prices revert, and little of lasting value remains.
Think subprime mortgages, portfolio insurance, or other fads that rise and fall without moving the world forward. These bubbles destroy wealth, full stop.
They form around technologies that genuinely change society: railroads, electricity, aviation, the internet, and now AI.
In these cases, the direction is right. The timing and pricing are often wrong.
In inflection bubbles, capital arrives faster than the technology can mature. Infrastructure is overbuilt, competition intensifies, and returns collapse, even as real-world adoption accelerates.
The world moves forward. But not all investors remain unscathed.
Marks makes a crucial point here: inflection bubbles can accelerate progress precisely because they waste capital. The speculative mania compresses decades of experimentation, trial-and-error, and infrastructure buildout into a short period. Much of the money is lost, but the foundation for future productivity is laid.
That creates a paradox investors often miss.
A technology can be world-changing and a terrible investment at certain prices.
Progress for society does not guarantee profits for shareholders.
AI clearly fits the inflection-bubble category. Its potential is real, and its impact is hard to dispute. But that doesn’t make every investment tied to AI sensible, or every valuation defensible.
Which leads to the most uncomfortable part of inflection bubbles: the end state is unknowable.
The defining feature of inflection bubbles is their unknowability.
Analysts already struggle with forecasting growth rates or margins for the next quarter. They’re being asked to price an end state that doesn’t yet exist, and may look nothing like today’s assumptions.
Google’s stock swung from uninvestable to inevitable in just a few years. Not because the future became predictable, but because new facts kept changing the narrative.
Howard Marks returns to this point repeatedly in his memo: the biggest risk isn’t being wrong about AI’s importance, but overestimating how much can be known in advance.
Technologies don’t move through the cycle uniformly. The Gartner Hype Cycle reminds us that expectations, adoption, and economic maturity rarely advance in lockstep. In AI, different layers are likely sitting in very different places, which makes pricing the “end state” especially fragile.
Start with the most basic question.
Who actually wins? History is brutal here. Revolutionary technologies don’t reward early leaders by default. Railroads, autos, search, and social media all reshuffled incumbents. Some of today’s frontrunners may dominate. Others may be displaced by companies that do not yet exist.
Who captures the profits? Even if AI adoption explodes, that doesn’t mean vendors earn excess returns. Productivity gains can accrue to customers instead. Cost savings may be competed away through lower prices. A technology can transform industries without enriching the companies that provide it.
What does the market structure look like? Monopoly, duopoly, competitive free-for-all, or a layered ecosystem with a few winners and many marginal players? Each outcome supports radically different valuations, yet markets often price one as if it’s inevitable.
How durable are today’s assets? Chips, data centers, and models are expensive, with a useful life still TBD. Rapid innovation increases the risk that today’s infrastructure becomes obsolete before it recoups its cost. That matters enormously when assigning multiples or underwriting long-term cash flows.
How much of the growth is real demand? Marks highlights the risk of circular behavior: vendors selling to customers who are simultaneously funding them, or partners transacting to show progress. Activity can look explosive without being durable. To be sure, Goldman Sachs estimates that less than 15% of NVIDIA’s revenue will come from circular deals in 2027, so it’s not the main story as some analysts make it out to be.
Layer these uncertainties together, and a pattern emerges.
The future may be enormous, but its shape, timing, and economics are deeply unclear.
Most bubbles deflate. Debt is what makes them dangerous.
When outcomes are uncertain, equity absorbs mistakes, delays, and pivots. Debt does not. It assumes cash flows will arrive on time and at scale to service fixed obligations. That’s a reasonable assumption in stable industries. It’s a fragile one in fast-moving technological revolutions.
This is where Howard Marks draws his sharpest line. Financing uncertainty with equity is normal. Financing conjecture with debt is not.
AI infrastructure sits uncomfortably close to that boundary. Chips, data centers, and models are expensive, capital-intensive assets with useful lives that are hard to forecast. Their economics depend on demand that may accelerate, stall, or shift as the technology evolves.
The dot-com era left behind dark fiber, massive infrastructure built for internet traffic that didn’t yet exist. It was a gamble on future demand that arrived too late.
In a recent a16z interview, Gavin Baker put it simply:
“Contrast that with today, there are no dark GPUs.”
AI looks different. We aren’t building ahead of demand. We are chasing it. GPU capacity is heavily utilized, and supply is constrained. Unlike fiber in 2000, today’s compute isn’t sitting idle.
High utilization acts as a floor. It reduces the risk of near-term write-downs and confirms that today’s CapEx is responding to genuine, cash-paying demand. But high utilization only proves the utility is real. It doesn’t prove the profitability is permanent.
And that doesn’t eliminate the role debt can play in turning uncertainty into fragility.
So far, much of the AI capex ramp has been funded internally. The largest platforms generate tens of billions of dollars in annual free cash flow from their existing businesses, allowing them to scale aggressively without immediately stressing their balance sheets.
Even OpenAI has largely relied on equity-based partnerships and long-term commercial agreements to finance its cash burn, avoiding near-term balance-sheet pressure.
At the same time, the bond market has quietly become part of the financing stack.
Meta issued one of the largest corporate bond deals in history, raising roughly $30 billion across long-dated maturities as AI capex accelerated.
Alphabet and Oracle have both issued 30-year bonds in recent years, explicitly extending financing horizons to match long-lived AI and cloud infrastructure.
Amazon, already one of the largest capital spenders in the world, continues to pair massive AWS CapEx with regular access to debt markets to preserve flexibility.
None of this is reckless in isolation. These are high-quality issuers with strong cash flows. Credit markets are open because default risk looks remote.
Compared to past bubbles, the system is less immediately fragile.
But it doesn’t make the capital immune to misallocation.
When CapEx is funded by free cash flow, the risk shifts from insolvency to dilution. Shareholders may avoid catastrophic outcomes while still paying through lower free cash flow, reduced buybacks, or years of subpar returns if investments fail to earn their cost of capital.
This is not 2000, but the buildout still comes at a cost.
We are facing a self-aware bubble today.
Investors openly debate whether the AI boom is a bubble. Valuation multiples are scrutinized. Comparisons to 1999 are everywhere. That awareness doesn’t eliminate excess.
In May 1999, Barron’s ran its now-famous “Amazon.bomb” cover, warning that Amazon’s business model was unproven.
Amazon’s stock fell more than 90% over the next three years.
Twenty-five years later, the stock is up more than 50×.
The lesson: Being right about the destination (AI will change the world) doesn’t protect you from the journey (a potential massive drawdown).
The job isn’t to predict the outcome, but to survive the volatility along the way. That requires conviction without rigidity. Strong views, loosely held. And a willingness to change your mind as facts change.
The hardest part of investing through a potential bubble isn’t data or analysis. It’s behavior. In legendary investor Peter Lynch's terms, the most crucial organ in investing is not the brain. It’s the stomach.
No one knows whether today’s enthusiasm fades quietly or ends painfully. Howard Marks borrows from Mark Twain and argues that history doesn’t repeat, but it often rhymes. Painful endings are more common than gentle ones. The trillion-dollar question is when.
Fed Chair Alan Greenspan famously coined the term “irrational exuberance” in 1996. The SP& 500 more than doubled from here before peaking in March 2000. The lesson is uncomfortable but clear: trying to call market tops is a hazardous hobby. Peter Lynch noted that more money is lost attempting to time corrections than in the corrections themselves.
Staying invested through uncertainty is a prerequisite for compounding returns. Yet it’s the rule most often abandoned.
One practical way to apply a long-term mindset is to assess how much optimism is already embedded in prices, particularly among market leaders. As Marks puts it, valuations have been “high, but not crazy,” particularly relative to the underlying business momentum.
Marks’ advice is deliberately unspectacular: not all-in, not all-out. A moderate position, applied with selectivity and prudence.
AI may prove to be the most important technology of our lifetime. It is also likely to produce excess, overbuilding, and painful corrections.
CEOs have no choice. They must over-invest to avoid extinction. Underinvesting is an existential risk they cannot take.
You do not face that constraint. You can sit on your hands. You can ignore the overpriced IPOs. You can diversify. That flexibility is not a weakness. It is your distinct advantage.
How to use it:
Separate belief from pricing: A company can change the world and still be a terrible investment at the wrong price.
Audit the balance sheet: In a storm, cash is oxygen. When capital becomes expensive, companies that need to borrow to survive often don’t.
Don’t mistake volatility for risk: Big drawdowns are the cost of admission in public markets. What Morgan Housel calls “a feature, not a bug.” You must have the stomach for 30%-50% drops to capture the long-term upside.
Avoid ruin: Stay away from leverage. Missing upside is uncomfortable, but being forced out is fatal. The goal is to stay invested long enough to benefit from what endures.
The investors who endure won’t be the loudest or the boldest. They will be the ones who stayed invested, selective, and humble. Long after the cycle has turned.
That's it for today.
Happy investing!
Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Save 15% with this link.
Disclosure: I own NVDA, AAPL, GOOG, AMZN, AVGO, and META in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-01-06 21:02:19
Welcome to the Premium edition of How They Make Money.
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In case you missed it:
The physical economy includes trucking, construction, and oil and gas. For decades, it has run on paper logs, phone calls, and manual processes.
Motive wants to be the operating system for this physical world.
Fresh off its S-1 filing on December 23, 2025, Motive is set to be one of the first major IPOs of 2026. It’s backed by heavyweight investors, including Google Ventures.
If you follow this space, you’re likely familiar with Samsara (IOT), a close peer that went public in 2021 and a holding in App Economy Portfolio. The two companies sell similar products and target the same fleet-heavy customers. But Motive enters the public markets with a very different financial profile.
Motive is smaller, growing more slowly, burning cash, and carrying meaningful debt.
Is Motive a way to play the digitization of the physical economy, or a runner-up trap in a winner-takes-most market?
I went through the nearly 300 pages of Motive’s S-1 so you don’t have to.
Today at a glance:
Overview
Business Model
Financial highlights
Risks & Challenges
Management
Use of Proceeds
Future Outlook
Personal Take
Motive is an Integrated Operations Platform for the physical economy.
Founded in 2013 by CEO Shoaib Makani as KeepTruckin, the company started with a simple wedge: a free app to help truck drivers log their hours on a smartphone.
The timing mattered. The ELD (Electronic Logging Device) mandate created a rare moment when software adoption became mandatory. When the rule became federal law in 2017, most commercial trucking fleets were pushed to digitize compliance. Motive positioned itself ahead of that shift, landing customers before compliance became mandatory.
Today, they have expanded far beyond compliance logs into a full ecosystem:
Driver Safety: AI dashcams that detect distracted driving, including phone usage and smoking.
Fleet Tracking: GPS tracking for vehicles and heavy equipment.
Spend Management: The Motive Card, a corporate card for fuel and fleet-related expenses that integrates directly with the platform.
Motive’s AI pitch is all about measurable outcomes. The company estimates that its platform has helped prevent more than 170,000 collisions since 2023, underscoring why safety and compliance budgets are often non-discretionary for fleet operators.
$501 million ARR (+27% Y/Y, accelerating).
100,000+ customers (mostly small trucking fleets).
4,500+ employees (heavy R&D presence in Pakistan).
For context, when Samsara went public in 2021, it did so with an ARR of $493 million (comparable to Motive) but was growing at nearly 70% Y/Y.
Motive operates a classic Hardware-Enabled SaaS model.
In short: Hardware is not the margin driver. It’s the lock-in.
Motive’s cameras, sensors, and in-vehicle devices aren’t optimized for margin. They exist to create continuous first-party operational data tied directly to physical activity. Once installed, that hardware makes Motive the system of record for safety and compliance, raises switching costs, and enables multi-year software expansion on top of the same data stream.
The wedge (hardware): Motive sells physical devices, including Vehicle Gateways (ELDs), AI dashcams, and Asset Gateways. These act as the “eyes and ears” of the fleet, continuously capturing data from drivers, vehicles, and equipment. Like Samsara, the hardware is often sold at or near cost to secure the long-term software relationship.
The subscription model (SaaS): Customers pay a per-vehicle, per-month subscription to access Motive’s cloud dashboard. Once installed, removing Motive hardware requires physical replacement across the fleet, making churn operationally painful. This creates high switching costs and predictable recurring revenue.
The fintech layer (transaction revenue): Motive’s newer growth lever is the Motive Card, which earns interchange revenue when drivers pay for fuel or repairs. By tying spend directly to fleet activity, Motive gains visibility into where money is spent, reduces fraud, and adds a transactional revenue stream on top of subscriptions.
The flywheel looks like this: Capture Data (IoT devices) > AI Insights (safety, efficiency) > Financial Services (Motive Card) > More Data.
Core Customers (ARR > $7.5K): 9,201 (+17% Y/Y)
Large Customers (ARR > $100K): 494 (+58% Y/Y).
Net Dollar Retention (NDR): 110% for Core Customers (+1pp Y/Y), and 126% for Large Customers. This shows they are successfully upselling larger fleets (adding dashcams, cards, etc.).
Expansion at Motive is usage-driven, not contract-driven. Revenue scales automatically as fleets grow in vehicles, drivers, assets, and locations. Larger fleets generate more data, adopt more modules, and standardize on fewer systems as operational complexity rises. That dynamic shows up clearly in the numbers, with large customers expanding far faster than smaller ones. Multi-product adoption is steadily improving among Core Customers.
So why is Motive growing at a much slower pace than Samsara at the same ARR milestone? The growth gap stems from the go-to-market strategy. Motive built its business in the SMB segment, initially selling low-ACV (Annual Contract Value) compliance solutions to small fleets. That left it with a large customer base, but lower revenue per account.
Samsara targeted mid-market and enterprise customers earlier, driving larger deal sizes and faster ARR expansion at a similar scale.
Let’s turn to the financials and where the money flows. Motive is growing, but it is burning cash to do it. And that burn is not improving with scale.
2026-01-02 21:02:26
Welcome to the Free edition of How They Make Money.
Over 260,000 subscribers turn to us for business and investment insights.
In case you missed it:
New year, same obsession: How market leaders actually make money.
While the internet is busy making bold predictions and hot takes, we’re starting the year the only way we know how: By following the money. 💰
Today at a glance:
📈 Sandisk Absolutely Crushed 2025
🧠 Meta Bets on AI Startup Manus
🤖 NVIDIA + Groq: The $20B Deal
🔎 Gemini’s Very Good Year
If you spent 2025 watching only the Mag 7, you missed one of the big stories of the year. The best-performing stock in the S&P 500 wasn’t a hyper-scaler or an LLM designer. It was a company that didn’t even exist as an independent entity at the start of the year.
Sandisk (SNDK) delivered a staggering ~594% return since its spinoff from Western Digital (WDC) in February 2025.
For years, Western Digital’s stock was dragged down by a conglomerate discount, trying to run a legacy Hard Drive business and a high-growth Flash business under one roof. The spinoff finally freed Sandisk to be valued on its own merits. It became a pure-play on the next phase of the AI buildout.
But a nearly 7x surge in under a year requires more than just a corporate reshuffle. It requires a fundamental misunderstanding by the market of how a company makes money. That misunderstanding was amplified by years of NAND price cyclicality, which trained investors to treat every storage rally as temporary.
Many investors still associate Sandisk with the little SD card in their camera. While that Consumer segment still provides cash flow, it’s no longer the engine.
Sandisk makes NAND Flash memory, the essential technology for fast, non-volatile storage. Their revenue is now driven by three distinct pillars:
Datacenter: High-performance Enterprise SSDs for hyperscalers (like AWS and Google Cloud). These deployments carry longer contracts, higher switching costs, and disproportionate influence on valuation by turning volatile NAND pricing into infrastructure-like revenue streams critical to AI training workloads.
Edge: It covers the critical flash memory inside AI Smartphones, PCs, Internet of Things (IoT), and autonomous vehicles. As devices run more AI models locally (on-device AI), they need massive storage upgrades.
Consumer: The portable SSDs and memory cards we all know.
Sandisk’s 2025 story was all about multiple expansion. The market stopped valuing Sandisk like a single-digit multiple NAND supplier and began pricing it closer to AI infrastructure peers with durable demand visibility.
In early 2025, the market valued Sandisk like a commodity hardware seller (a low P/E multiple). By mid-year, the narrative shifted. Investors realized that if AI is the new electricity, high-speed storage is the copper wire beneath it. Invisible, essential, and impossible to scale without.
Sandisk was “re-rated” from a boring hardware stock to critical AI infrastructure.
This re-rating was so powerful that the stock ripped higher even as gross margins compressed. Why? Investors recognized that Sandisk was executing a deliberate land grab, accepting near-term margin pressure as it ramps enterprise volumes and secures long-term hyperscaler relationships with data centers building out their AI architecture for the next decade.
What’s the moat? Sandisk’s advantage relies on the high switching costs of the "Qualification Cycle." In the AI era, hyperscalers cannot simply swap out storage components on a whim. Drives must undergo grueling 6-to-12-month testing regimens to ensure they don't cause latency spikes that idle expensive GPUs. Once Sandisk wins a socket and becomes the "Plan of Record," it locks in that revenue for the server's entire 3-to-5-year lifecycle. Furthermore, while competitors like Samsung are prioritizing HBM capacity for GPUs, Sandisk has capitalized on the less crowded lane of Enterprise SSDs, using specialized controllers and firmware to turn what used to be a commodity component into a sticky, essential layer of the AI infrastructure stack.
Takeaway: Spinoffs often unlock massive value by stripping away narrative noise and forcing markets to reprice the underlying economics. But more importantly, 2025 proved that the boring infrastructure layers of a tech boom can be far more lucrative than the flashy consumer apps built on top of them.
Meta Platforms just made another bold AI move, agreeing to acquire Manus for more than $2 billion, a deal reportedly agreed in just 10 days.
It might sound like a steep price, but Manus isn’t a typical chatbot. It’s a general-purpose AI agent designed to plan tasks, pull in tools, and deliver finished work. Think research reports, code, data analysis, or even websites, with minimal human input.
Manus was founded in China in 2022 and later moved to Singapore. It officially launched its agent in March 2025.
~100 Manus employees.
~$85 million raised so far.
~$125 million in ARR (crossed $100 million within 8 months of launch).
That puts Manus in rare territory for enterprise software adoption.
Meta plans to continue selling Manus as a standalone product while integrating its agent capabilities across Meta’s ecosystem: Facebook, Instagram, WhatsApp, ads, and AI hardware.
Meta has world-class models and massive distribution, but it has lagged peers like ChatGPT and Gemini in real, task-oriented AI applications. Manus fills that gap immediately.
Instead of waiting years to build an agent platform from scratch, Meta is buying:
A proven agent workflow.
Paying enterprise customers.
A subscription revenue stream.
A team already shipping in production.
It also gives Meta a path to meaningful AI revenue beyond ads.
Takeaway: This deal is all about monetization. Meta is using its balance sheet to shortcut the hardest part of AI: turning impressive technology into products businesses actually pay for. At $2 billion, Manus is expensive. But at $125 million in ARR in under a year, it gives Meta something rare in AI today: traction with economics attached.
In a move that stunned Wall Street over the holiday break, NVIDIA announced a definitive agreement to pay $20 billion in cash to license the technology and hire the core engineering team of AI chip startup Groq.
This is not a traditional acquisition. Groq will continue to exist as an independent entity (renamed GroqCloud), led by former CFO Simon Edwards. However, NVIDIA is effectively absorbing Groq’s “soul”—its proprietary LPU (Language Processing Unit) patents and its visionary leadership, including founder Jonathan Ross (creator of the Google TPU).
Why now? To understand this deal, you have to look at the market shift that defined late 2025. We recently crossed a tipping point known as the Inference Flip. Global revenue from using AI models (inference) has officially surpassed the revenue from building them (training).
While NVIDIA’s GPUs are the undisputed kings of training (massive throughput), they have a vulnerability: Latency.
NVIDIA GPUs are like freight trains. Incredible capacity to haul massive data, but slow to get up to speed.
Groq LPUs are like Formula 1 cars. Lightweight, instant acceleration, and designed purely for real-time speed.
By securing Groq’s technology, NVIDIA solves its only real weakness: real-time, low-latency inference for agents and voice AI.
What it means:
Regulatory shenanigans: By structuring this as a “licensing and hiring” deal rather than a merger, NVIDIA sidesteps the antitrust gridlock that killed the ARM deal. It mirrors the strategy Microsoft used with Inflection AI earlier this year.
Capital efficiency: $20 billion sounds like a lot, but for NVIDIA, it represents just one quarter of Free Cash Flow. They essentially used three months of profit to neutralize a systemic threat and secure the next decade of inference dominance.
The moat widens: This deal deprives competitors (like Google and AMD) of some of the industry’s best inference talent. NVIDIA is no longer just selling shovels for the AI gold rush. It now owns the distribution network for the gold itself.
Takeaway: NVIDIA just signaled that it refuses to be disrupted from below. By integrating Groq’s speed into the upcoming Rubin architecture, NVIDIA ensures it remains the default operating system for the entire AI economy—from massive training runs to split-second inferencing.
For most of 2024, the GenAI story looked simple: ChatGPT dominated, everyone else scrambled.
2025 quietly complicated that narrative.
Released on Christmas Day, new data from Similarweb tells one of the most important AI stories of the year:
Gemini is approaching a 20% share of GenAI website traffic
ChatGPT has slipped from 87% a year ago to below 70% today.
The shift comes down to distribution.
Gemini now sits inside Google’s core products, including Search, Chrome, Android, and Workspace. It’s turning default placement into habitual usage. When AI lives where people already spend their time, adoption follows naturally.
Two signals stand out from SimilarWeb’s data:
Grok continues to gain traction, helped by tight integration with X and real-time content.
The market is consolidating into a ‘Big Two’ dynamic, with ChatGPT and Gemini pulling away from a long tail of task-specific tools.
Gemini is the only product meaningfully taking share, and it’s doing so via Google distribution and defaults. As model quality converges, power users might show little loyalty to a single interface. They switch to whatever is cheapest, fastest, or already embedded in their workflow.
That challenges the idea of a lasting moat at the AI interface layer.
OpenAI doesn’t expect to be profitable until 2030 and, according to Deutsche Bank, could burn more than $140 billion in cumulative negative free cash flow before getting there. Google’s parent Alphabet, by contrast, generated $74 billion in free cash flow over the past 12 months, even after heavy AI investment.
That asymmetry matters.
Takeaway: ChatGPT built the category and forced Google to respond. Now Google is using its balance sheet, distribution, and pricing power to compete aggressively, while OpenAI is still figuring out its unit economics. The clock is ticking on whether ChatGPT can meaningfully pressure Search before Gemini closes the gap.
That's it for today.
Happy investing!
Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Save 15% with this link.
Disclosure: I own GOOG, META, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2025-12-30 12:00:50
Welcome to the Premium edition of How They Make Money.
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🔥 The December report is here!
All the key earnings visuals from the past month in one report.
✔️ Cut through the noise with clear, concise financial snapshots.
✔️ See revenue trends, profit margins, and key takeaways instantly.
We visualized 200+ companies this season:
In case you missed it:
Download the full report below or log in to your account.
Your voice matters! Help us shape future reports. Got a company or sector you're curious about? Hit 'Reply' and let us know!
Here’s a sneak peek. 👀
What to expect in our monthly report?
🛒 Retail: Costco.
🌯 Franchises: Darden.
🥫 FMCG: General Mills.
👔 Consulting: Accenture.
😎 Tourism: Carnival, Vail Resorts.
💰 New IPOs: Medline, Wealthfront.
⚙️ Semis: Broadcom, Micron, Marvell.
🎽 Apparel: Nike, Lululemon, Birkenstock.
☁️ Productivity: Asana, DocuSign, GitLab, UiPath.
🛡️ Cybersecurity: CrowdStrike, Okta, SentinelOne.
💼 Enterprise Software: Adobe, Oracle, Salesforce.
📊 Data: C3.ai, HPE, MongoDB, Rubrik, Samsara, Snowflake.
And more, like Chewy, FedEx, GameStop, HP, and HealthEquity.
2025-12-26 21:01:09
Welcome to the Free edition of How They Make Money.
Over 260,000 subscribers turn to us for business and investment insights.
Today, we revisit some of the most popular articles published by App Economy Insights in 2025.
I publish articles across two services:
How They Make Money (via Substack):
Weekly business breakdowns for visual thinkers.
Over 200 companies visualized quarterly for Premium members.
Weekly earnings updates on 200+ market leaders for PRO members.
App Economy Portfolio (via Seeking Alpha):
Popular investing group where I share my entire stock portfolio.
Monthly deep dives, live trades, and watch lists.
Timely quarterly updates on 70+ holdings.
Stock ratings (BUY, SELL, or HOLD).
It’s the last post of 2025. So, it’s time to reflect!
It’s been another incredible year for App Economy Insights:
216 articles published, including:
52 free posts on How They Make Money.
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Before we start 2026, here are 12 of the most popular posts of 2025.
2025 was another great year to be an investor.
Chances are, you are pretty happy with your portfolio today. After a catastrophic 2022, the market rose over 20% in 2023 and 2024, and now 18% in 2025.
The S&P benchmark returned an average annual 10% from 1926 to 2024, and 74% of years were positive. The longer the time horizon, the higher the chance of positive returns. If you want to stack the deck in your favor, the easiest step by far is to trade less and ignore the doomsayers.
“Pessimists sound smart. Optimists make money.”
I share my stock portfolio with a community of long-term-focused investors. So I wanted to share the best and worst-performing investments this year:
📈 Best investment: AppLovin (APP): In March 2025, I wrote: “Several short reports have targeted the company, but they generally come from low-level analysts or content creators with no reputation or research teams. Conversely, hedge funds like Baillie Gifford, Coatue, and Sands Capital have invested in APP.” The market has been underestimating the leverage within AppLovin’s software platform. As the AI-driven AXON engine continues to optimize ad matching, we are seeing a decoupling of revenue growth from operational costs, leading to a massive repricing. The stock has nearly tripled since then, dramatically outperforming the market.
📉 Worst investment: The Trade Desk (TTD): I added to my existing position in February 2025, aiming to capitalize on what appeared to be a reasonable entry point. Since then, the stock has struggled with sentiment shifts in the ad-tech category and a slowdown in revenue growth, partially explained by tough comps from US political spending in 2024. The stock is down 50% from that specific purchase, making it our worst investment this year. TTD has also been the worst performer in the S&P 500. But consider this: TTD has been a holding since 2017, and despite the recent drawdown, the stock price remains over 10x our initial purchase price. It's another great reminder, if you needed one, that zooming out and keeping a long-term view is critical in investing.
You can expect How They Make Money to expand even more in 2026.
What business or topic do you want to learn about?
Let us know in the comments or reply to any of my emails. I read everything!
Thank you for tagging along!
I wish you and yours a wonderful 2026. ✨
That’s it for today!
Stay healthy and invest on!
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Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
Disclosure: I am long AAPL, AMZN, GOOG, META, PLTR, UBER, and TSLA in the App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members here.
2025-12-23 21:02:04
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